International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
KNEE OSTEOARTHRITIS DETECTION AND CLASSIFICATION USING XRAY Sasikala G1 , Elakkiya P2, Haemamabujavalli G3, Indhu P4, Divyadharani S5 1Assistant Professor, Vivekanandha College of Engineering for Women, , Thiruchengode, Tamil Nadu(India) 2345 Student of Vivekanandha
College of Engineering for Women, Thiruchengode, Tamil Nadu(India) --------------------------------------------------------------------------------***-------------------------------------------------------------------------------Abstract— Knee osteoarthritis is a degenerative classification of knee osteoarthritis severity using gait data joint disease that affects millions worldwide. Early and radiographic images [12]. Automatic Detection and detection and classification are crucial for effective Classification of Knee Osteoarthritis Using Hu’s Invariant treatment and management. This study proposes a Moments [13]. Automatic Detection and Classification of computer-aided diagnosis system using X-ray images Knee Osteoarthritis Using Hu’s Invariant Moments [14]. to detect and classify knee osteoarthritis. The system The results demonstrate that the proposed method can employs deep learning techniques to analyze X-ray effectively reduce the noise level of radiographic images images and classify the severity of osteoarthritis based and improve the accuracy of osteoarthritis classification. on standardized radiographic criteria. The results KEYWORDS:. Convolutional Neural Networks (CNNs), show high accuracy in detecting osteoarthritis and presymptomatic cartilage, leukemic B-lymphoblast classifying its severity, demonstrating the potential of classification, Knee Osteoarthritis. this system to assist clinicians in early diagnosis and treatment planning. A new reality of transforming II. SYSTEM ANALYSIS diagnostic medicine. An aggregated-based deep learning method for leukemic B-lymphoblast A. EXISTING SYSTEM classification. Classification using deep-neuralnetwork-based features. Automated classification of Currently, the diagnosis and classification of knee radiographic knee osteoarthritis severity using deep osteoarthritis primarily rely on manual interpretation of Xneural networks. Enabling early detection of ray images by radiologists and orthopedic specialists. This osteoarthritis from presymptomatic cartilage texture process is time-consuming and subjective, leading to maps via transport-based learning. variability in diagnoses and potential delays in treatment [1]. Moreover, the expertise required for accurate I. INTRODUCTION interpretation may not always be readily available, particularly in remote or underserved areas. As a result, Knee osteoarthritis represents a significant health there is a growing need for automated systems that can burden globally [1]. posing challenges in diagnosis and assist healthcare providers in the efficient and consistent treatment due to its progressive nature [2]. Early detection analysis of medical imaging data. and accurate classification are pivotal for effective management and improved patient outcomes[4]. In this context, leveraging advancements in deep learning techniques, particularly Convolutional Neural Networks (CNNs), offers promising avenues for enhancing knee OA classification [5]. This project aims to develop a robust classification system utilizing CNNs, complemented by integration into a Flask web application for practical deployment [6]. By harnessing the power of CNNs and Flask, the project seeks to provide healthcare professionals with a reliable tool for automating knee OA classification, thereby streamlining diagnostic processes and facilitating timely interventions [7]. This introduction sets the stage for exploring the methodologies and outcomes of the proposed approach, contributing to the advancement of medical diagnostics and personalized healthcare [8]. Knee osteoarthritis severity classification with ordinal regression module [9]. Towards shape-based knee osteoarthritis classification using graph convolutional network. [10] In Proceedings of the International Symposium on Biomedical Imaging (ISBI) [11]. Machine learning- based automatic
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B. PROPOSED SYSTEM The proposed system aims to address the limitations of the existing approaches by leveraging deep learning techniques, specifically YOLOv8 (You Only Look Once version 8), for brain tumor detection[2]. Unlike traditional methods that rely on manual feature engineering, the proposed system adopts a data-driven approach, allowing the model to automatically learn discriminative features directly from raw medical imaging data. By utilizing the YOLOv8 architecture, which offers real-time object detection capabilities with high accuracy, the proposed system can efficiently identify and localize brain tumors in MRI scans[3]. Additionally, the proposed system integrates advanced preprocessing techniques and data augmentation strategies to enhance the quality of input images and improve the robustness of the model to variability in tumor phenotypes and imaging conditions. Overall, the proposed system aims to provide a more efficient, scalable, and
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